SUICIDE RATES OVERVIEW | 1985 TO 2016

This compiled dataset pulled from four other datasets linked by time and place, and was built to find signals correlated to increased suicide rates among different cohorts globally, across the socio-economic spectrum.

EXPLORATORY DATA ANALYSIS

Data Preparation and Cleaning

In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
from sklearn import preprocessing

sns.set(style='darkgrid')
In [2]:
df = pd.read_csv(r"D:\Misc\Suicide_Rates_Overview_1985_to_2016.csv")
In [3]:
df.head(10)
Out[3]:
country year sex age suicides_no population suicides/100k pop country-year HDI for year gdp_for_year ($) gdp_per_capita ($) generation
0 Albania 1987 male 15-24 years 21 312900 6.71 Albania1987 NaN 2,156,624,900 796 Generation X
1 Albania 1987 male 35-54 years 16 308000 5.19 Albania1987 NaN 2,156,624,900 796 Silent
2 Albania 1987 female 15-24 years 14 289700 4.83 Albania1987 NaN 2,156,624,900 796 Generation X
3 Albania 1987 male 75+ years 1 21800 4.59 Albania1987 NaN 2,156,624,900 796 G.I. Generation
4 Albania 1987 male 25-34 years 9 274300 3.28 Albania1987 NaN 2,156,624,900 796 Boomers
5 Albania 1987 female 75+ years 1 35600 2.81 Albania1987 NaN 2,156,624,900 796 G.I. Generation
6 Albania 1987 female 35-54 years 6 278800 2.15 Albania1987 NaN 2,156,624,900 796 Silent
7 Albania 1987 female 25-34 years 4 257200 1.56 Albania1987 NaN 2,156,624,900 796 Boomers
8 Albania 1987 male 55-74 years 1 137500 0.73 Albania1987 NaN 2,156,624,900 796 G.I. Generation
9 Albania 1987 female 5-14 years 0 311000 0.00 Albania1987 NaN 2,156,624,900 796 Generation X
In [7]:
df.tail(10)
Out[7]:
country year sex age suicides_no population suicides/100k pop country_year HDI for year gdp_for_year ($) gdp_per_capita generation
27810 Uzbekistan 2014 female 15-24 years 347 2992817 11.59 Uzbekistan2014 0.675 63,067,077,179 2309 Millenials
27811 Uzbekistan 2014 male 55-74 years 144 1271111 11.33 Uzbekistan2014 0.675 63,067,077,179 2309 Boomers
27812 Uzbekistan 2014 male 15-24 years 347 3126905 11.10 Uzbekistan2014 0.675 63,067,077,179 2309 Millenials
27813 Uzbekistan 2014 male 75+ years 17 224995 7.56 Uzbekistan2014 0.675 63,067,077,179 2309 Silent
27814 Uzbekistan 2014 female 25-34 years 162 2735238 5.92 Uzbekistan2014 0.675 63,067,077,179 2309 Millenials
27815 Uzbekistan 2014 female 35-54 years 107 3620833 2.96 Uzbekistan2014 0.675 63,067,077,179 2309 Generation X
27816 Uzbekistan 2014 female 75+ years 9 348465 2.58 Uzbekistan2014 0.675 63,067,077,179 2309 Silent
27817 Uzbekistan 2014 male 5-14 years 60 2762158 2.17 Uzbekistan2014 0.675 63,067,077,179 2309 Generation Z
27818 Uzbekistan 2014 female 5-14 years 44 2631600 1.67 Uzbekistan2014 0.675 63,067,077,179 2309 Generation Z
27819 Uzbekistan 2014 female 55-74 years 21 1438935 1.46 Uzbekistan2014 0.675 63,067,077,179 2309 Boomers
In [4]:
df.rename({' gdp_for_year ($ ) ' : 'gdp_for_year', 'gdp_per_capita ($)' : 'gdp_per_capita', 'country-year' : 'country_year'}, axis=1, inplace=True)
df
Out[4]:
country year sex age suicides_no population suicides/100k pop country_year HDI for year gdp_for_year ($) gdp_per_capita generation
0 Albania 1987 male 15-24 years 21 312900 6.71 Albania1987 NaN 2,156,624,900 796 Generation X
1 Albania 1987 male 35-54 years 16 308000 5.19 Albania1987 NaN 2,156,624,900 796 Silent
2 Albania 1987 female 15-24 years 14 289700 4.83 Albania1987 NaN 2,156,624,900 796 Generation X
3 Albania 1987 male 75+ years 1 21800 4.59 Albania1987 NaN 2,156,624,900 796 G.I. Generation
4 Albania 1987 male 25-34 years 9 274300 3.28 Albania1987 NaN 2,156,624,900 796 Boomers
... ... ... ... ... ... ... ... ... ... ... ... ...
27815 Uzbekistan 2014 female 35-54 years 107 3620833 2.96 Uzbekistan2014 0.675 63,067,077,179 2309 Generation X
27816 Uzbekistan 2014 female 75+ years 9 348465 2.58 Uzbekistan2014 0.675 63,067,077,179 2309 Silent
27817 Uzbekistan 2014 male 5-14 years 60 2762158 2.17 Uzbekistan2014 0.675 63,067,077,179 2309 Generation Z
27818 Uzbekistan 2014 female 5-14 years 44 2631600 1.67 Uzbekistan2014 0.675 63,067,077,179 2309 Generation Z
27819 Uzbekistan 2014 female 55-74 years 21 1438935 1.46 Uzbekistan2014 0.675 63,067,077,179 2309 Boomers

27820 rows × 12 columns

In [8]:
df.columns
Out[8]:
Index(['country', 'year', 'sex', 'age', 'suicides_no', 'population',
       'suicides/100k pop', 'country_year', 'HDI for year',
       ' gdp_for_year ($) ', 'gdp_per_capita', 'generation'],
      dtype='object')
  • What is the suicide rates per 100k population by gdp per capita?
  • What is the suicide rates per country per year?
  • What is the suicide rates per country by age
  • What is the suicide rates per country by sex
  • What is the suicide rates per country by sex and age?
  • What is the suicide rates per country by sex, generation and age?
In [10]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 27820 entries, 0 to 27819
Data columns (total 12 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   country             27820 non-null  object 
 1   year                27820 non-null  int64  
 2   sex                 27820 non-null  object 
 3   age                 27820 non-null  object 
 4   suicides_no         27820 non-null  int64  
 5   population          27820 non-null  int64  
 6   suicides/100k pop   27820 non-null  float64
 7   country_year        27820 non-null  object 
 8   HDI for year        8364 non-null   float64
 9    gdp_for_year ($)   27820 non-null  object 
 10  gdp_per_capita      27820 non-null  int64  
 11  generation          27820 non-null  object 
dtypes: float64(2), int64(4), object(6)
memory usage: 2.5+ MB
In [11]:
df.describe()
Out[11]:
year suicides_no population suicides/100k pop HDI for year gdp_per_capita
count 27820.000000 27820.000000 2.782000e+04 27820.000000 8364.000000 27820.000000
mean 2001.258375 242.574407 1.844794e+06 12.816097 0.776601 16866.464414
std 8.469055 902.047917 3.911779e+06 18.961511 0.093367 18887.576472
min 1985.000000 0.000000 2.780000e+02 0.000000 0.483000 251.000000
25% 1995.000000 3.000000 9.749850e+04 0.920000 0.713000 3447.000000
50% 2002.000000 25.000000 4.301500e+05 5.990000 0.779000 9372.000000
75% 2008.000000 131.000000 1.486143e+06 16.620000 0.855000 24874.000000
max 2016.000000 22338.000000 4.380521e+07 224.970000 0.944000 126352.000000
In [12]:
df.corr()
Out[12]:
year suicides_no population suicides/100k pop HDI for year gdp_per_capita
year 1.000000 -0.004546 0.008850 -0.039037 0.366786 0.339134
suicides_no -0.004546 1.000000 0.616162 0.306604 0.151399 0.061330
population 0.008850 0.616162 1.000000 0.008285 0.102943 0.081510
suicides/100k pop -0.039037 0.306604 0.008285 1.000000 0.074279 0.001785
HDI for year 0.366786 0.151399 0.102943 0.074279 1.000000 0.771228
gdp_per_capita 0.339134 0.061330 0.081510 0.001785 0.771228 1.000000

Visualization

Plot the correlation

In [14]:
plt.figure(figsize=(16,6))
heatmap = sns.heatmap(df.corr(), vmin=-1, vmax= 1, annot=True, cmap='BrBG')
heatmap.set_title('Correlation Heatmap', fontdict={'fontsize':12}, pad=12);
In [15]:
df.age.unique()
Out[15]:
array(['15-24 years', '35-54 years', '75+ years', '25-34 years',
       '55-74 years', '5-14 years'], dtype=object)
In [16]:
df.suicides_no.value_counts()
Out[16]:
0       4281
1       1539
2       1102
3        867
4        696
        ... 
2158       1
525        1
2297       1
5241       1
2872       1
Name: suicides_no, Length: 2084, dtype: int64
In [17]:
missing_percentages = df.isna().sum().sort_values(ascending= False) / len(df)
missing_percentages
Out[17]:
HDI for year          0.699353
country               0.000000
year                  0.000000
sex                   0.000000
age                   0.000000
suicides_no           0.000000
population            0.000000
suicides/100k pop     0.000000
country_year          0.000000
 gdp_for_year ($)     0.000000
gdp_per_capita        0.000000
generation            0.000000
dtype: float64

Missing percentages

In [13]:
missing_percentages[missing_percentages != 0].plot(kind = 'barh')
Out[13]:
<AxesSubplot:>

Delete the HDI for year column since it has many missing values

In [18]:
del df['HDI for year']
In [20]:
df.columns
Out[20]:
Index(['country', 'year', 'sex', 'age', 'suicides_no', 'population',
       'suicides/100k pop', 'country_year', ' gdp_for_year ($) ',
       'gdp_per_capita', 'generation'],
      dtype='object')

Unique Countries

In [21]:
countries =df.country.unique()
countries
Out[21]:
array(['Albania', 'Antigua and Barbuda', 'Argentina', 'Armenia', 'Aruba',
       'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain',
       'Barbados', 'Belarus', 'Belgium', 'Belize',
       'Bosnia and Herzegovina', 'Brazil', 'Bulgaria', 'Cabo Verde',
       'Canada', 'Chile', 'Colombia', 'Costa Rica', 'Croatia', 'Cuba',
       'Cyprus', 'Czech Republic', 'Denmark', 'Dominica', 'Ecuador',
       'El Salvador', 'Estonia', 'Fiji', 'Finland', 'France', 'Georgia',
       'Germany', 'Greece', 'Grenada', 'Guatemala', 'Guyana', 'Hungary',
       'Iceland', 'Ireland', 'Israel', 'Italy', 'Jamaica', 'Japan',
       'Kazakhstan', 'Kiribati', 'Kuwait', 'Kyrgyzstan', 'Latvia',
       'Lithuania', 'Luxembourg', 'Macau', 'Maldives', 'Malta',
       'Mauritius', 'Mexico', 'Mongolia', 'Montenegro', 'Netherlands',
       'New Zealand', 'Nicaragua', 'Norway', 'Oman', 'Panama', 'Paraguay',
       'Philippines', 'Poland', 'Portugal', 'Puerto Rico', 'Qatar',
       'Republic of Korea', 'Romania', 'Russian Federation',
       'Saint Kitts and Nevis', 'Saint Lucia',
       'Saint Vincent and Grenadines', 'San Marino', 'Serbia',
       'Seychelles', 'Singapore', 'Slovakia', 'Slovenia', 'South Africa',
       'Spain', 'Sri Lanka', 'Suriname', 'Sweden', 'Switzerland',
       'Thailand', 'Trinidad and Tobago', 'Turkey', 'Turkmenistan',
       'Ukraine', 'United Arab Emirates', 'United Kingdom',
       'United States', 'Uruguay', 'Uzbekistan'], dtype=object)

Age Count

In [22]:
age_count = df.age
age_count
Out[22]:
0        15-24 years
1        35-54 years
2        15-24 years
3          75+ years
4        25-34 years
            ...     
27815    35-54 years
27816      75+ years
27817     5-14 years
27818     5-14 years
27819    55-74 years
Name: age, Length: 27820, dtype: object
In [23]:
suicides_per_100k = (df['suicides/100k pop'].sum()) / len(df['suicides/100k pop'])
suicides_per_100k
Out[23]:
12.816097411933864

Suicides per 100K population is approx 12.82

In [24]:
suicides_number  = df.suicides_no[:30]
sns.histplot(data = suicides_number, kde=True, bins = 20)
plt.show()
In [25]:
px.histogram(suicides_number)

Sex count

In [298]:
px.pie(df, names='sex')
In [286]:
fig = px.histogram(
    df,
    x='sex',
    y='population'
)
fig.show()
In [144]:
px.histogram(
    df, x='sex', 
    y='suicides_no',
    title='SUICIDE BY GENDER',
    labels={'sex': 'Gender', 'suicides_no': 'Suicides Number'}
)
In [145]:
px.histogram(
    df, x='sex', 
    y='suicides/100k pop',
    title='SUICIDES PER 100K POPULATION BY GENDER',
    labels={'sex': 'Gender'}
)
In [256]:
fig = px.histogram(
    df,
    x='gdp_per_capita',
    y='suicides/100k pop',
    color='sex',
    marginal='box',
    height=600,
    title='SUICIDES BY GDP PER CAPITA AND SEX',
    labels={'gdp_per_capita': 'GDP per Capita'}
)
fig.show()
In [269]:
fig = px.histogram(
    df,
    x='year',
    y='suicides/100k pop',
    height=500,
    marginal='box',
    title='SUICIDES BY YEAR AND GENDER',
    labels={'year': 'Year'}
)
#fig.update_layout(bargap=0.1)
fig.show()
In [147]:
fig = px.histogram(
    df,
    x='year',
    y='suicides_no',
    color='sex',
    height=500,
    marginal='box',
    title='SUICIDES BY YEAR AND GENDER',
    labels={'year': 'Year'}
)
#fig.update_layout(bargap=0.1)
fig.show()
In [300]:
fig = px.histogram(
    df,
    x='year',
    y='suicides/100k pop',
    color='sex',
    height=600,
    marginal='box',
    title='SUICIDES PER 100K BY YEAR AND GENDER',
    labels={'year': 'Year'}
)
#fig.update_layout(bargap=0.1)
fig.show()
In [149]:
fig = px.histogram(df, 
                   x='age', 
                   y='suicides_no',
                   color='sex',
                   title='SUICIDES BY AGE AND SEX',
                   height=500,
                   labels={'age': 'Age'}
)
fig.show()
In [150]:
fig = px.histogram(df, 
                   x='age', 
                   y='suicides/100k pop',
                   color='sex',
                   title='SUICIDES PER 100K BY AGE AND SEX',
                   height=500,
                   labels={'age': 'Age'}
)
fig.show()
In [151]:
fig = px.histogram(
    df,
    x='generation',
    y='suicides_no',
    color='sex',
    height=500,
    title='SUICIDES BY GENERATION AND GENDER',
    labels={'generation': 'Generation'}
)
fig.show()
In [152]:
fig = px.histogram(
    df,
    x='generation',
    y='suicides/100k pop',
    color='sex',
    height=500,
    title='SUICIDES PER 100K BY GENERATION AND GENDER',
    labels={'generation': 'Generation'}
)
fig.show()
In [261]:
fig = px.histogram(
    df,
    x='year',
    y='suicides/100k pop',
    color='generation',
    height=600,
    marginal='box',
    title='SUICIDES PER 100K BY YEAR AND GENDER',
    labels={'year': 'Year'}
)
#fig.update_layout(bargap=0.1)
fig.show()
In [106]:
fig = px.scatter(
    df,
    x='year',
    y='suicides/100k pop',
    color='sex',
    height=500
)
fig.show()
In [126]:
fig = px.histogram(
    df,
    x='country',
    y='suicides/100k pop',
    color='sex',
    height=600,
    width=1050,
    title='SUICIDES PER 100K BY COUNTRY '
)
fig.show()
In [287]:
fig = px.scatter(
    df,
    x='country',
    y='suicides/100k pop',
    color='generation',
    height=600,
    title='SUICIDES PER 100K BY COUNTRY ',
)
fig.show()
In [260]:
fig = px.histogram(
    df,
    x='country',
    y='suicides/100k pop',
    color='age',
    height=600,
    title='SUICIDES PER 100K BY COUNTRY '
)
fig.show()
In [159]:
df[' gdp_for_year ($) ']
Out[159]:
0         2,156,624,900
1         2,156,624,900
2         2,156,624,900
3         2,156,624,900
4         2,156,624,900
              ...      
27815    63,067,077,179
27816    63,067,077,179
27817    63,067,077,179
27818    63,067,077,179
27819    63,067,077,179
Name:  gdp_for_year ($) , Length: 27820, dtype: object
In [198]:
fig = px.scatter(df.query("year==1995"), 
                 x="gdp_per_capita", 
                 y="suicides/100k pop",
                 size="population", 
                 color="generation",
                 hover_name="country", 
                 log_x=True, 
                 size_max=60,
                 height=500
)
fig.show()
In [226]:
fig = px.scatter(df.query("year==2007"), 
                 x="gdp_per_capita", 
                 y="suicides/100k pop",
                 size="population", 
                 color="sex",
                 hover_name="country", 
                 log_x=True, 
                 size_max=60,
                 height=500
)
fig.show()
In [228]:
fig = px.scatter(df.query("year==1995"), 
                 y="suicides_no", 
                 x="gdp_per_capita",
                 size="suicides/100k pop", 
                 color="generation",
                 hover_name="country", 
                 log_x=True, 
                 size_max=60,
                 height=500,
                 title='SUICIDES BY GDP PER CAPITA'
)
fig.show()
In [242]:
fig = px.histogram(
    df,
    x='year',
    y='gdp_per_capita',
    height=500,
    title='GDP PER CAPITA BY YEAR'
)
fig.show()
In [257]:
fig = px.histogram(
    df,
    y='suicides/100k pop',
    x='gdp_per_capita',
    color='sex',
    marginal='box',
    height=600
)
fig.show()
In [267]:
fig = px.histogram(
    df,
    x='country',
    y='suicides/100k pop',
    color='sex',
    height=600,
    #marginal='box',
    title='SUICIDES PER 100K BY YEAR AND GENDER',
    labels={'year': 'Year'}
)
#fig.update_layout(bargap=0.1)
fig.show()
In [ ]:
In [190]:
df.columns
Out[190]:
Index(['country', 'year', 'sex', 'age', 'suicides_no', 'population',
       'suicides/100k pop', 'country_year', ' gdp_for_year ($) ',
       'gdp_per_capita', 'generation'],
      dtype='object')
In [44]:
plt.figure(figsize=(18,6))
ax = sns.lineplot(x='country', hue='age', y="suicides_no", data=df)
plt.xlabel("Country", fontsize = 14)
plt.ylabel("Suicide No", fontsize = 14)
plt.xticks(rotation=90)
plt.xlim(0, )
plt.title("Suicide by Country and Age", fontsize = 16, font = 'Roboto')
Out[44]:
Text(0.5, 1.0, 'Suicide by Country and Age')
In [45]:
plt.figure(figsize=(18,6))
ax = sns.lineplot(x='country', hue='age', y="suicides/100k pop", data=df, errorbar=None)
plt.xlabel("Country", fontsize = 14)
plt.ylabel("Suicide per 100K", fontsize = 14)
plt.xticks(rotation=90)
plt.xlim(0, )
plt.title("Suicide per 100K by Country and Age", fontsize = 16, font = 'Roboto')
Out[45]:
Text(0.5, 1.0, 'Suicide per 100K by Country and Age')

Conclusion

  • Suicides per 100K population is approximately 12.82
  • There has been an equal gender distribution
  • Suicide rate per 100K population between 1985 - 2016 was highest among males than females. Russian federation has the highest total male suicides.
  • Suicides per 100K population increase with age
  • Suicide rate is highest among Boomers.
  • Suicides per 100K population peaked in 1995, dropping significantly between 1995 and 2015
  • Russian Federation and United States account for highest number of suicides. United States, also with highest population is the second highest in total suicide count.